CodeCarbon (mlco2/codecarbon)

CodeCarbon

https://github.com/mlco2/codecarbon
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CodeCarbon is a Python package that estimates and tracks carbon emissions from computer code...

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# CodeCarbon

CodeCarbon is a lightweight Python package that estimates and tracks carbon dioxide (CO₂) emissions from computing resources. It measures the electricity power consumption of hardware components (CPU, GPU, and RAM) and applies the carbon intensity of the electricity grid in the region where the computation is running. This allows developers and researchers to quantify the environmental impact of their machine learning training, data processing, or any compute-intensive code.

The package supports both online mode (with automatic geolocation and real-time carbon intensity data) and offline mode (for air-gapped environments). It provides multiple tracking methods including decorators, context managers, and explicit object APIs, making it easy to integrate into any Python workflow. Results can be saved to CSV files, sent to the CodeCarbon API dashboard, pushed to Prometheus, Logfire, or custom HTTP endpoints, enabling comprehensive monitoring and reporting of carbon footprints across projects and experiments.

## Installation

Install CodeCarbon from PyPI with pip.

```bash
pip install codecarbon

# For visualization dashboard support
pip install 'codecarbon[carbonboard]'
```

## EmissionsTracker Class

The `EmissionsTracker` class is the primary interface for tracking carbon emissions in online mode. It automatically detects hardware, geolocation, and carbon intensity data from the internet.

```python
from codecarbon import EmissionsTracker

# Basic usage with explicit start/stop
tracker = EmissionsTracker(
    project_name="my_ml_project",
    measure_power_secs=15,          # Measure power every 15 seconds
    output_dir="./emissions",       # Directory for CSV output
    output_file="emissions.csv",    # CSV filename
    save_to_file=True,              # Save results to CSV
    save_to_api=False,              # Send to CodeCarbon API
    tracking_mode="machine",        # "machine" or "process"
    log_level="info",               # Logging verbosity
)

tracker.start()
try:
    # Your compute-intensive code here
    import time
    for epoch in range(10):
        print(f"Training epoch {epoch}")
        time.sleep(2)  # Simulated training
finally:
    emissions = tracker.stop()  # Returns total CO2 emissions in kg

print(f"Total emissions: {emissions * 1000:.4f} g CO2eq")
print(f"Duration: {tracker.final_emissions_data.duration:.2f} seconds")
print(f"Energy consumed: {tracker.final_emissions_data.energy_consumed:.6f} kWh")
print(f"CPU energy: {tracker.final_emissions_data.cpu_energy:.6f} kWh")
print(f"GPU energy: {tracker.final_emissions_data.gpu_energy:.6f} kWh")
print(f"RAM energy: {tracker.final_emissions_data.ram_energy:.6f} kWh")
```

## EmissionsTracker as Context Manager

The context manager pattern provides automatic start/stop handling and is the recommended approach for tracking emissions in most scenarios.

```python
from codecarbon import EmissionsTracker
import tensorflow as tf

# Load data
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

# Build model
model = tf.keras.models.Sequential([
    tf.keras.layers.Flatten(input_shape=(28, 28)),
    tf.keras.layers.Dense(128, activation="relu"),
    tf.keras.layers.Dropout(0.2),
    tf.keras.layers.Dense(10),
])
model.compile(optimizer="adam",
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=["accuracy"])

# Track emissions with context manager
with EmissionsTracker(project_name="mnist_training") as tracker:
    model.fit(x_train, y_train, epochs=10)

# Access results after context exits
print(f"Carbon emissions: {tracker.final_emissions * 1000:.4f} g CO2eq")
print(f"Detailed data: {tracker.final_emissions_data}")
```

## @track_emissions Decorator

The decorator pattern enables tracking with minimal code changes, ideal for wrapping training functions or scripts.

```python
from codecarbon import track_emissions
import time

@track_emissions(
    project_name="model_training",
    output_dir="./logs",
    save_to_file=True,
    save_to_api=False,
    measure_power_secs=10,
    log_level="warning",
)
def train_model():
    """Training function wrapped with emissions tracking."""
    # Simulate model training
    print("Starting training...")
    for epoch in range(5):
        time.sleep(3)  # Simulated epoch
        print(f"Epoch {epoch + 1} complete")
    print("Training finished!")
    return "trained_model"

if __name__ == "__main__":
    model = train_model()
    # Emissions are automatically logged to ./logs/emissions.csv
```

## OfflineEmissionsTracker

The `OfflineEmissionsTracker` is designed for environments without internet access. It requires a country ISO code or cloud provider/region to estimate carbon intensity.

```python
from codecarbon import OfflineEmissionsTracker

# Offline tracking with country ISO code
tracker = OfflineEmissionsTracker(
    project_name="offline_experiment",
    country_iso_code="USA",          # 3-letter ISO country code
    region="california",             # Optional: state/province (for US/Canada)
    measure_power_secs=15,
    output_dir=".",
    save_to_file=True,
)

tracker.start()
try:
    # Your compute code
    import numpy as np
    data = np.random.randn(10000, 10000)
    result = np.dot(data, data.T)
finally:
    emissions = tracker.stop()

print(f"Offline emissions: {emissions * 1000:.4f} g CO2eq")

# Offline tracking with cloud provider
cloud_tracker = OfflineEmissionsTracker(
    project_name="cloud_experiment",
    cloud_provider="aws",            # aws, gcp, or azure
    cloud_region="us-east-1",        # Cloud region identifier
)
```

## @track_emissions Decorator in Offline Mode

The decorator also supports offline mode by passing the `offline` parameter and required location information.

```python
from codecarbon import track_emissions

@track_emissions(
    offline=True,
    country_iso_code="CAN",          # Canada
    region="ontario",                # Province
    project_name="offline_training",
    measure_power_secs=10,
)
def train_offline():
    """Train model in an air-gapped environment."""
    import time
    for i in range(10):
        time.sleep(1)
    return "model_trained"

if __name__ == "__main__":
    train_offline()
```

## Task-Based Tracking

Task tracking allows measuring emissions for specific code sections within a larger experiment. This is useful for comparing different phases like data loading, model building, and inference.

```python
from codecarbon import EmissionsTracker
import time

def load_dataset():
    time.sleep(2)
    return {"train": [1, 2, 3], "test": [4, 5, 6]}

def build_model():
    time.sleep(2)
    return "trained_model"

def run_inference(model, data):
    time.sleep(1)
    return "predictions"

# Track individual tasks
tracker = EmissionsTracker(project_name="ml_pipeline", measure_power_secs=5)

try:
    # Task 1: Load dataset
    tracker.start_task("load_dataset")
    dataset = load_dataset()
    task1_emissions = tracker.stop_task()

    # Task 2: Build model
    tracker.start_task("build_model")
    model = build_model()
    task2_emissions = tracker.stop_task()

    # Task 3: Run inference (multiple iterations)
    for i in range(3):
        tracker.start_task(f"inference_{i}")
        predictions = run_inference(model, dataset["test"])
        task_emissions = tracker.stop_task()
        print(f"Inference {i}: {task_emissions.emissions * 1000:.4f} g CO2eq")

finally:
    total_emissions = tracker.stop()

# Print task-level breakdown
print(f"\nTotal emissions: {total_emissions * 1000:.4f} g CO2eq")
for task_name, task in tracker._tasks.items():
    print(f"  {task_name}: {task.emissions_data.emissions * 1000:.4f} g CO2eq "
          f"({task.emissions_data.duration:.1f}s)")
```

## TaskEmissionsTracker Context Manager

The `TaskEmissionsTracker` provides a context manager for tracking individual tasks within an existing tracker.

```python
from codecarbon import EmissionsTracker, TaskEmissionsTracker
import time

# Create main tracker
tracker = EmissionsTracker(project_name="task_demo")
tracker.start()

try:
    # Track specific tasks using context manager
    with TaskEmissionsTracker(task_name="data_preprocessing", tracker=tracker):
        time.sleep(2)  # Simulate preprocessing

    with TaskEmissionsTracker(task_name="model_training", tracker=tracker):
        time.sleep(3)  # Simulate training

    with TaskEmissionsTracker(task_name="evaluation", tracker=tracker):
        time.sleep(1)  # Simulate evaluation

finally:
    emissions = tracker.stop()
    print(f"Total: {emissions * 1000:.4f} g CO2eq")
```

## flush() Method

The `flush()` method writes current emissions data to outputs (CSV, API, etc.) without stopping the tracker. Useful for long-running experiments where you want periodic checkpoints.

```python
from codecarbon import EmissionsTracker
import time

tracker = EmissionsTracker(
    project_name="long_training",
    on_csv_write="append",  # "append" adds new row, "update" overwrites existing
)
tracker.start()

try:
    for epoch in range(100):
        time.sleep(1)  # Simulate epoch training

        # Flush every 10 epochs to save progress
        if (epoch + 1) % 10 == 0:
            current_emissions = tracker.flush()
            print(f"Epoch {epoch + 1}: {current_emissions * 1000:.4f} g CO2eq cumulative")

finally:
    total = tracker.stop()
    print(f"Final total: {total * 1000:.4f} g CO2eq")
```

## Configuration File

CodeCarbon supports hierarchical configuration through config files, environment variables, and code parameters. Priority: script > env vars > local config > global config.

```ini
# ~/.codecarbon.config (global) or ./.codecarbon.config (local)
[codecarbon]
measure_power_secs = 15
output_dir = ./emissions
output_file = emissions.csv
save_to_file = true
save_to_api = false
log_level = INFO
tracking_mode = machine
project_name = my_project

# For API usage
api_endpoint = https://api.codecarbon.io
experiment_id = your-experiment-uuid
api_key = your-api-key

# For offline mode
country_iso_code = USA
region = california

# Advanced settings
pue = 1.2                          # Power Usage Effectiveness
wue = 1.8                          # Water Usage Effectiveness (L/kWh)
force_cpu_power = 65               # Override CPU TDP in watts
force_ram_power = 20               # Override RAM power in watts
gpu_ids = 0,1                      # Track specific GPUs only
allow_multiple_runs = true         # Allow concurrent trackers
```

```python
# Using environment variables
import os
os.environ["CODECARBON_PROJECT_NAME"] = "env_project"
os.environ["CODECARBON_LOG_LEVEL"] = "DEBUG"
os.environ["CODECARBON_GPU_IDS"] = "0,1"

from codecarbon import EmissionsTracker
tracker = EmissionsTracker()  # Uses environment variables
```

## Command Line Interface

CodeCarbon provides CLI commands for monitoring, configuration, and hardware detection.

```bash
# Check version
codecarbon --version

# Configure CodeCarbon interactively (creates .codecarbon.config)
codecarbon config

# Login to CodeCarbon API
codecarbon login

# Detect hardware capabilities
codecarbon detect
# Output:
# - Available RAM: 32.000 GB
# - CPU count: 16 thread(s) in 8 physical CPU(s)
# - CPU model: Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz
# - GPU count: 2
# - GPU model: NVIDIA GeForce RTX 3080

# Monitor machine emissions continuously (Ctrl+C to stop)
codecarbon monitor

# Monitor with custom settings
codecarbon monitor --measure-power-secs 10 --api-call-interval 30

# Monitor without API (local only)
codecarbon monitor --no-api

# Monitor in offline mode
codecarbon monitor --offline --country-iso-code FRA

# Monitor while running a specific command
codecarbon monitor -- python train.py
codecarbon monitor -- ./benchmark.sh
codecarbon monitor -- npm run build

# Get API token for a project
codecarbon get-token <project-id>
```

## Saving to CodeCarbon API

Send emissions data to the CodeCarbon dashboard for centralized tracking and visualization across experiments.

```python
from codecarbon import track_emissions, EmissionsTracker

# Using decorator with API
@track_emissions(
    save_to_api=True,
    experiment_id="your-experiment-uuid",  # From codecarbon config or dashboard
    api_key="your-api-key",
    api_call_interval=4,                   # Send to API every 4 measurements
    measure_power_secs=30,
)
def train_with_api():
    import time
    for i in range(10):
        time.sleep(5)

# Using EmissionsTracker with API
tracker = EmissionsTracker(
    save_to_api=True,
    api_endpoint="https://api.codecarbon.io",
    experiment_id="your-experiment-uuid",
    api_key="your-api-key",
    api_call_interval=8,  # -1 to only call at end
)

tracker.start()
try:
    # Training code
    pass
finally:
    tracker.stop()
```

## Prometheus Integration

Push emissions metrics to Prometheus for monitoring and alerting. Metrics are prefixed with `codecarbon_`.

```python
from codecarbon import EmissionsTracker, OfflineEmissionsTracker
import os

# Set authentication if required
os.environ["PROMETHEUS_USERNAME"] = "your_username"
os.environ["PROMETHEUS_PASSWORD"] = "your_password"

tracker = OfflineEmissionsTracker(
    project_name="prometheus_demo",
    country_iso_code="USA",
    save_to_prometheus=True,
    prometheus_url="localhost:9091",  # Pushgateway URL
    save_to_file=True,
)

tracker.start()
try:
    import time
    for i in range(10):
        time.sleep(2)
finally:
    tracker.stop()

# Metrics available in Prometheus:
# - codecarbon_emissions_total
# - codecarbon_energy_consumed_total
# - codecarbon_cpu_power
# - codecarbon_gpu_power
# - codecarbon_ram_power
```

## Logfire Integration

Send emissions data to Pydantic Logfire observability platform.

```python
from codecarbon import EmissionsTracker

tracker = EmissionsTracker(
    project_name="logfire_demo",
    save_to_logfire=True,  # First run prompts for Logfire login
    save_to_file=True,
)

tracker.start()
try:
    import time
    time.sleep(10)
finally:
    tracker.stop()

# Metrics appear in Logfire as codecarbon_* format
```

## Logger Output

Send emissions data to Python logging or Google Cloud Logging for centralized log management.

```python
import logging
from codecarbon import EmissionsTracker
from codecarbon.output import LoggerOutput

# Create a dedicated logger
emissions_logger = logging.getLogger("emissions")
emissions_logger.setLevel(logging.INFO)

# Add file handler
file_handler = logging.FileHandler("emissions.log")
file_handler.setFormatter(logging.Formatter('%(asctime)s - %(message)s'))
emissions_logger.addHandler(file_handler)

# Create LoggerOutput wrapper
logger_output = LoggerOutput(emissions_logger, logging.INFO)

# Use with EmissionsTracker
tracker = EmissionsTracker(
    project_name="logging_demo",
    save_to_logger=True,
    logging_logger=logger_output,
    save_to_file=False,  # Disable CSV since we're using logger
)

tracker.start()
try:
    import time
    time.sleep(5)
finally:
    tracker.stop()
```

```python
# Google Cloud Logging example
import google.cloud.logging
from codecarbon import EmissionsTracker
from codecarbon.output import GoogleCloudLoggerOutput

client = google.cloud.logging.Client(project="my-gcp-project")
gcp_logger = GoogleCloudLoggerOutput(client.logger("codecarbon-emissions"))

tracker = EmissionsTracker(
    save_to_logger=True,
    logging_logger=gcp_logger,
)
```

## HTTP Webhook Output

Send emissions data to a custom HTTP endpoint when tracking stops.

```python
from codecarbon import EmissionsTracker

tracker = EmissionsTracker(
    project_name="webhook_demo",
    emissions_endpoint="https://your-server.com/api/emissions",
    save_to_file=True,
)

tracker.start()
try:
    import time
    time.sleep(5)
finally:
    tracker.stop()
    # POST request sent to emissions_endpoint with JSON emissions data
```

## Custom Output Handlers

Create custom output handlers by extending the `BaseOutput` class.

```python
from codecarbon.output import BaseOutput
from codecarbon.output_methods.emissions_data import EmissionsData

class DatabaseOutput(BaseOutput):
    """Custom output handler to save emissions to a database."""

    def __init__(self, connection_string):
        self.connection_string = connection_string
        # Initialize database connection

    def out(self, total: EmissionsData, delta: EmissionsData):
        """Called when tracker.stop() or tracker.flush() is invoked."""
        print(f"Saving to database: {total.emissions} kg CO2eq")
        # Save total emissions to database

    def live_out(self, total: EmissionsData, delta: EmissionsData):
        """Called periodically during tracking (every api_call_interval measurements)."""
        print(f"Live update: {delta.emissions} kg CO2eq (delta)")
        # Stream delta emissions to database

    def task_out(self, task_emissions_list, experiment_name):
        """Called when tasks are tracked."""
        for task_data in task_emissions_list:
            print(f"Task: {task_data}")

    def exit(self):
        """Called when tracker stops - cleanup resources."""
        # Close database connection
        pass

# Use custom handler
from codecarbon import EmissionsTracker

db_output = DatabaseOutput("postgresql://localhost/emissions")
tracker = EmissionsTracker(
    output_handlers=[db_output],
    save_to_file=False,
)
```

## Hardware Detection

Programmatically detect and inspect hardware configuration.

```python
from codecarbon import EmissionsTracker

# Create tracker without starting it
tracker = EmissionsTracker(save_to_file=False)

# Get detected hardware info
hardware = tracker.get_detected_hardware()

print(f"RAM: {hardware['ram_total_size']:.2f} GB")
print(f"CPU threads: {hardware['cpu_count']}")
print(f"Physical CPUs: {hardware['cpu_physical_count']}")
print(f"CPU model: {hardware['cpu_model']}")
print(f"GPU count: {hardware['gpu_count']}")
print(f"GPU model: {hardware['gpu_model']}")
print(f"Tracked GPU IDs: {hardware['gpu_ids']}")

# Example output:
# RAM: 32.00 GB
# CPU threads: 16
# Physical CPUs: 8
# CPU model: Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz
# GPU count: 2
# GPU model: 2 x NVIDIA GeForce RTX 3080
# Tracked GPU IDs: None
```

## GPU Selection

Track specific GPUs instead of all available GPUs on the system.

```python
from codecarbon import EmissionsTracker
import os

# Method 1: Using CUDA_VISIBLE_DEVICES (auto-detected)
os.environ["CUDA_VISIBLE_DEVICES"] = "0,2"

# Method 2: Using gpu_ids parameter (overrides CUDA_VISIBLE_DEVICES)
tracker = EmissionsTracker(
    project_name="multi_gpu",
    gpu_ids=[0, 1],  # Track only GPU 0 and 1
    # Or as string: gpu_ids="0,1"
)

tracker.start()
try:
    # GPU training code
    pass
finally:
    tracker.stop()
```

## PUE and WUE Configuration

Configure Power Usage Effectiveness (PUE) and Water Usage Effectiveness (WUE) for datacenter operations.

```python
from codecarbon import EmissionsTracker

# Track with datacenter efficiency factors
tracker = EmissionsTracker(
    project_name="datacenter_tracking",
    pue=1.2,        # PUE multiplier (typical range: 1.1 - 2.2)
    wue=1.8,        # Water consumption: liters per kWh
)

tracker.start()
try:
    import time
    time.sleep(10)
finally:
    emissions = tracker.stop()

# Access water consumption data
print(f"Emissions: {tracker.final_emissions_data.emissions * 1000:.4f} g CO2eq")
print(f"Water consumed: {tracker.final_emissions_data.water_consumed:.4f} liters")
print(f"Energy consumed: {tracker.final_emissions_data.energy_consumed:.6f} kWh")
```

## Carbonboard Visualization

Visualize emissions data locally using the built-in Dash dashboard.

```bash
# Install visualization dependencies
pip install 'codecarbon[carbonboard]'

# Launch visualization dashboard
carbonboard --filepath="./emissions.csv" --port=8050

# Open browser to http://localhost:8050
```

```python
# Generate emissions data for visualization
from codecarbon import EmissionsTracker
import time

for project in ["project_a", "project_b", "project_c"]:
    tracker = EmissionsTracker(
        project_name=project,
        output_file="emissions.csv",
    )
    tracker.start()
    time.sleep(5)
    tracker.stop()

# Then visualize: carbonboard --filepath="./emissions.csv"
```

## EmissionsData Structure

The `EmissionsData` dataclass contains all tracked metrics returned by `tracker.final_emissions_data`.

```python
from codecarbon import EmissionsTracker
import time

tracker = EmissionsTracker()
tracker.start()
time.sleep(5)
tracker.stop()

data = tracker.final_emissions_data

# Core emissions metrics
print(f"Timestamp: {data.timestamp}")
print(f"Duration: {data.duration} seconds")
print(f"Emissions: {data.emissions} kg CO2eq")
print(f"Emissions rate: {data.emissions_rate} kg/s")

# Energy breakdown
print(f"Total energy: {data.energy_consumed} kWh")
print(f"CPU energy: {data.cpu_energy} kWh")
print(f"GPU energy: {data.gpu_energy} kWh")
print(f"RAM energy: {data.ram_energy} kWh")

# Power averages
print(f"CPU power: {data.cpu_power} W")
print(f"GPU power: {data.gpu_power} W")
print(f"RAM power: {data.ram_power} W")

# Utilization metrics
print(f"CPU utilization: {data.cpu_utilization_percent}%")
print(f"GPU utilization: {data.gpu_utilization_percent}%")
print(f"RAM utilization: {data.ram_utilization_percent}%")
print(f"RAM used: {data.ram_used_gb} GB")

# Location info
print(f"Country: {data.country_name} ({data.country_iso_code})")
print(f"Region: {data.region}")
print(f"On cloud: {data.on_cloud}")
print(f"Cloud provider: {data.cloud_provider}")
print(f"Cloud region: {data.cloud_region}")

# System info
print(f"OS: {data.os}")
print(f"Python: {data.python_version}")
print(f"CodeCarbon: {data.codecarbon_version}")
print(f"CPU model: {data.cpu_model}")
print(f"CPU count: {data.cpu_count}")
print(f"GPU model: {data.gpu_model}")
print(f"GPU count: {data.gpu_count}")
print(f"RAM total: {data.ram_total_size} GB")

# Export to JSON
print(data.toJSON())
```

## Comet.ml Integration

Automatically track carbon emissions alongside ML experiments in Comet.ml.

```python
# pip install comet_ml>=3.2.2
from comet_ml import Experiment
import tensorflow as tf

# Initialize Comet experiment (auto-creates EmissionsTracker)
experiment = Experiment(api_key="YOUR_COMET_API_KEY")

# Your training code
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

model = tf.keras.models.Sequential([
    tf.keras.layers.Flatten(input_shape=(28, 28)),
    tf.keras.layers.Dense(128, activation="relu"),
    tf.keras.layers.Dense(10),
])

model.compile(optimizer="adam",
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True))
model.fit(x_train, y_train, epochs=5)

experiment.end()
# View CodeCarbon panel in Comet.ml dashboard
```

## Electricity Maps API Integration

Use real-time carbon intensity data from Electricity Maps for more accurate emissions estimates.

```python
from codecarbon import EmissionsTracker

# With Electricity Maps API token (get from electricitymaps.com)
tracker = EmissionsTracker(
    project_name="realtime_carbon",
    electricitymaps_api_token="your-api-token",
)

tracker.start()
try:
    import time
    time.sleep(10)
finally:
    tracker.stop()
```

CodeCarbon is an essential tool for researchers and developers who want to understand and reduce the environmental impact of their computing workloads. It seamlessly integrates into existing Python workflows through decorators, context managers, or explicit tracking objects, requiring minimal code changes while providing detailed insights into energy consumption and carbon emissions.

The package supports diverse deployment scenarios including local development machines, cloud instances (AWS, GCP, Azure), and air-gapped environments. With built-in support for multiple output destinations (CSV, API, Prometheus, Logfire, custom handlers), CodeCarbon enables comprehensive monitoring and reporting. Whether tracking a single training run or monitoring an entire ML pipeline with task-level granularity, CodeCarbon provides the visibility needed to make informed decisions about computational sustainability.